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On the Politics of “Collective Intelligence” in Electronic Networks

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The
term “collective intelligence,” particularly within leftist discourses, leads
one to expect a certain obvious and direct connection to discussions on
collectivity, general intellect, the emancipatory potential of cooperation,
etc. The term comes up time and again, be it in the work of Virno,[1]
Negri/Hardt,[2]
Rancière,[3] or on
the website of the platform UniNomade, which once described itself as an
“adventure in collective intelligence.”[4]
Despite the overwhelmingly positive and even euphoric responses to it,[5]
however, it remains marginal.

Searching
for further uses of the term can lead one to very peculiar topics, ranging from
neoliberal business models, to naïve communitarianism, and even esotericism and
parapsychology, and the question soon arises if the term can even be used
within leftist contexts at all. Here, I will take a closer look at the use of the
term within discourses surrounding current production forms and labor
realities,[6]
primarily in the context of electronic networks.

My
analysis begins at the dawn of the 21st century, as the technical
and conceptual developments of the Internet are intensifying to the point that
a qualitatively new situation emerges, which quickly falls under the buzzword
“Web 2.0” and later more broadly under “social media.”

The Effective
Individual

The
story leading up to this, however, goes back at least forty more years. The
expression “collective intelligence” was presumably first used in the
mid-1970s, in connection with communication and cooperation using networked
computers,[7] in
the context of the research and praxis of what is known as “Computer Supported
Cooperative Work” (CSCW)[8],
which was just being formed at the time.

Its
pioneer, Douglas Engelbart, created a research center in California in the
early 1960s, which examined the augmentation of the human intellect using
computers. On the one hand, the Augmentation Research Center still
worked in the tradition of Taylorist time and motion studies, using these means
of optimizing assembly-line work in order to determine the ergonomic advantages
of the computer mouse in comparison to other pointing tools, for instance.[9]

By the
same token, however, it also went far beyond this to questions of labor
organization and teamwork. They not only developed communication and
cooperation tools that were incorporated into the online-system NLS, but also
initiated an experiment in which the research team itself was the object of
examination, which Engelbart later described as “a behavioral
science experiment as well as a computer systems experiment.”[10]

Approaches
that were rooted in the protest movements of the 1960s were also integrated
into the experiment, ranging from yoga to New Age personality development
seminars, to consulting Mao’s Red Book for concepts of
revolutionization/innovation.[11] Just
as the group was finally about to become the focal point of the research work,
the Research Center broke up. This was due, on the one hand, to the
contradiction between the reactionary institutional backing through military
funds and the collaboration with corporations, and, on the other, to the
growing political radicalization among the young staff members.[12]

What’s
interesting about this pioneer endeavor in cooperation and teamwork using
computers is that the individual is placed at its starting point, at the center
of a new knowledge order, more or less the “invention of the user.”[13]
Engelbart explained this in a lecture at a documentation and information
science conference in 1960, where he demanded that in addition to the
objectivist systematization of knowledge, such as bibliographic systems and the
like, research on the organization of information should include a perspective
that considered the individual.[14]

Ten
years later, Engelbart sets in relation to the “individual” the term “knowledge
worker,” drawing on management theorist Peter Drucker’s definition: “the person
who creates and applies knowledge to productive ends, in contrast to an
‘intellectual’ for whom information and concepts may only have importance
because they interest him.”[15] In
many areas of the later discourses surrounding “collective intelligence,” the
orientation of knowledge toward “productive goals” is at the core of the
definition of “intelligence.”

The
individual—as variety and as the essential horizon of all collectivity—is also
found in the first major publication within the humanities on the topic. In his
book Collective Intelligence, Mankind’s Emerging World in Cyberspace,
published in 1994, Pierre Lévy states: “the basis and goal of collective
intelligence is the mutual recognition and enrichment of individuals rather
than the cult of fetishized or hypostatized communities.”[16]

“Bottom-up Revolution”

Back
in the 21st century: the most obvious focal points in the debate on
“collective intelligence” were the developments related to the Internet, which
also led to a rapid increase in participatory possibilities. When Tim O’Reilly,
in a text from 2005 that brought the term Web 2.0 into circulation, declares
that the main strength of those Internet corporations, which continued to boom
despite the crash of 2001, lies in harnessing “collective intelligence”, and
therefore in exploiting a new resource, through which the software industry was
able to reconsolidate itself,[17] then
the significance of “collective intelligence” as an exploitable resource
becomes perfectly obvious.

In the
literature on the subject, a canon of famous examples for “collective
intelligence” soon developed, covering a range of methods, from ones based
mainly on statistic aggregation models, such as Google’s PageRank algorithm
(which views every link pointing to a website as a voting for its relevance
and—weighted according to incoming links on the original website—uses this as a
main criterion for the sequence of search results[18]), to
models largely defined by conscious decisions made by the users, such as
Wikipedia.

Organizational
theory and management praxis are also key reference points. The rapidly
improving possibilities for “automating” the coordination of cooperation with
the help of software and the construction of electronic networks reduce the
necessity within a corporation to integrate them into rigid hierarchies and
tendentially also serve to open up the organization’s boundaries.

The
aspect that theories of post-Fordism and cognitive capitalism have critically
examined, namely that this “automation” heavily relies on the employees’
potential to self-organize, vanishes behind undifferentiated notions of a
general “bottom-up revolution”[19]. At
the same time, technology and methodology are constantly developing and newer
solutions are emerging that make coordinating comparatively simple tasks
possible exclusively through software, especially if they are outsourced to the
Internet.

The
most fundamental condition that teamwork organization must create—namely
breaking up the task into rational, practical parts that can then easily be
aggregated again—has been optimized to such a point within specific IT fields
that the tasks have been pulverized into the tiniest elements, thereby creating
the conditions for people with very different capacities and available time
resources to contribute to completing the overall task.

The
concepts and practices of “collective
intelligence” are expanded through and parallel to “Web 2.0” and “social
media,” not only because many users are involved in Web-offers of this kind
within the “participatory Web,” but also because the ways these models are
disseminated explicitly calls for them to be copied. A paper from the “Center
for Collective Intelligence,” which came out of the Management School at the
renowned MIT, explicitly analyzes different models using a modular system of
genes and genomes, in order to facilitate their reproduction.[20]

I
would like to briefly elaborate on two areas: first, on the concept of
crowdsourcing, i.e. outsourcing tasks to the Internet, whereby “collective
intelligence” becomes intricately connected to developments that are also being
theorized under the term “immaterial labor,” and second, the prediction market
hype within postmodern management that strongly links it to neoliberal
ideology.

From
“Business by Accident” to the “Home Sweatshop”

The
strength of the open source movement was already visible in the 1990s. While
one competing company after another foundered on Microsoft’s near-monopoly
position, open source projects proved absolutely capable of surviving–the Linux
operating system and the later, highly successful Apache Web server are perhaps
the most prominent examples.

Two
different conclusions could be drawn here. There is the tendentially
anti-capitalist interpretation—which, however, in pracitce is commonly argued
in a way that conforms to the system—that sees a new logic of social production
emerging, which does not adhere to the laws of competition, proprietorship and
profit orientation. On the other hand, the phenomenon could also be interpreted
and employed completely in terms of capitalism, namely, as a form of organizing
production in a way that is potentially more efficient than that of a enterprise.

The
term “crowdsourcing,” which supposedly first appeared in 2006, represents the
latter pro-capitalist position through its primary emphasis on profit-oriented
aspects. The term, however, is not only restricted to this aspect, as it also
attempts to embrace the phenomenon in its entirety. In this way and through its “outside perspective” on the “crowd”
or “community,” the term carries much more meaning than its mere relation to profit
orientation.

Particularly
early on in the open source and free software movements, the online community
was the source of the perspective, so to speak. This was the perspective from
which decisions concerning the usefulness of certain tools, developments, etc.
were made, and their reputation system did not simply fulfill a function in the
production process, but served as a key horizon in regards to the participants’
involvement.

In
contrast, simply the fact that crowdsourcing alludes to “outsourcing” is a
clear indication that the intended action perspective greatly differs from the
crowd/community as a production site, thus shifting the perspective from
content-related self-organization toward management.

In a
“status update” in the 2009 edition of his book, Jeff Howe, who presumably
coined the term, views the implications of the concept much more dismally than
in the first edition from the year before. In the meantime, the financial
crisis had prompted corporations to further cut costs, and had simultaneously
increased the scores of unemployed, thus prominently highlighting the negative
aspects of crowdsourcing.

The
picture is no longer characterized by the dazzling stories of multi-million
IT-companies that emerged more or less by accident from the ideas of a few
clever young graduates who just wanted to do something cool for their
community, but instead by the bitter realities of harsher working conditions:
“We could well be seeing the emergence of the home sweatshop, with people’s
productivity and work habits closely monitored via their computers. Two years
ago such a vision seemed ridiculous on its face. Now it strikes me as
inescapable.”[21]

In his
book, Howe views the emergence of a new kind of amateur as one of the four
conditions of crowdsourcing,[22] which
he attributes to the broader access to education after World War II. What Carlo
Vercellone deems to be a working-class victory and takes as the starting point
for his analysis of cognitive capitalism,[23] Howe
views as the “overeducation of the middle class.”[24] There
are plenty of examples for this new kind of amateur with a field-related
education and sometimes also work experience that parallels that of
professionals:[25] from
the over-abundance of art students considering the corresponding labor markets
to chemists working as financial consultants, and to scientists who, after a
dull day at the laboratory, hope to find a tricky question on InnoCentive.[26]

In a
manner that reflects Virno’s analysis—that realizing the general intellect
merely in production and not in political self-organization leads to the
uncontrollable spread of hierarchies[27]—Howe
views the problems in certain labor markets on a general level, while solution
strategies can only be employed on an individual level. For Howe, there are no
solidary/collective solutions for the unemployed artist and for the chemist who
is frustrated by the daily lab routine, only isolated and individual ways.

In
crowdsourcing, many elements that fall under the term “immaterial labor” appear
in more concentrated forms: incorporation of the “whole person,” the
exploitation of self-organization, and the full deregulation of work hours and
places. At the same time, this exploitation model is not connected with the
image of overcoming the factory system, but instead it represents more a
generalization of the (self-)exploitation mechanisms commonly found in art and
in the creative industries: self-realization, enjoying work, interesting
assignments in exchange for otherwise utterly unacceptable working conditions.

Neoliberal
Oracles

Generally,
there is a broad scope of methods for aggregating information and predictions
developed in conjunction with concepts of “collective intelligence,” ranging
from the successful use of simple averaging in specific contexts to complex
nonlinear functions.

Prediction
markets generally mimic stock markets for this purpose, as the following
example of predicting the outcome of the presidential elections briefly
explains:

“Consider
a contract that pays $1 if Candidate X wins the presidential election [...]. If
the market price of an X contract is currently 53 cents, an interpretation is
that the market ‘believes’ X has a 53% chance of winning. Prediction markets
reflect a fundamental principle underlying the value of market-based pricing:
Because information is often widely dispersed among economic actors, it is
highly desirable to find a mechanism to collect and aggregate that information.
Free markets usually manage this process well because almost anyone can
participate, and the potential for profit (and loss) creates strong incentives
to search for better information.”[28]

Principally,
this form is applied on two levels, namely, in publically accessible Internet
platforms where, for instance, the outcomes of elections are predicted, as in
the example cited above, and in various areas within companies, particularly in
conjunction with new products, from choosing ideas for products with the best
chances on the market, to assessing opportune moments for introducing a product
to the market and estimating sales figures.

On the
one hand, prediction markets emerged as one of the most prominent examples of
“collective intelligence” because various corporations have been applying and
testing them within their companies since the 1990s, and not least, because
there are several examples that can easily be conveyed, such as the Iowa Electronic
Markets’ (IEM) successful election predictions.[29]

Employing
these methods within companies tends to trigger effects of “immaterial labor,”
such as individual breaches—made possible by the relative anonymity on a
collective level—of disclosing otherwise “hidden” information, ranging from the
head office’s pessimistic outlook on being able to keep a production date, to
knowing a colleague’s private plans to quit. The secrets don’t have to be
“revealed,” but they are incorporated into the predictions. Then again, there
are structural “threats” to a company’s hierarchies that range from the
necessity to reveal crucial data, to the problems that high-paid managers face
when working with methods that are ultimately based on the assumption that
collectives make better decisions.

While
in practice the initial euphoria fizzled out into the mundane integration of
prediction markets into the standard repertoire of advanced management, on a
broader level, the ideological effects of a method that is still acclaimed as a
cutting-edge management method remain. Prediction markets can be portrayed as an
innovative method that responds to how financial markets operate. They tend to
outshine conventional social, economic and political methods, such as group
discussions,[30]
meetings and opinion polls, and represent neoliberal economic ideology in its
purest form. It’s no coincidence that the often very rudimentary depiction of
the conceptual basis invokes the idealization of market mechanisms found in
Hayek’s classic formulations of neoliberal ideologies. Especially in terms of
“collective intelligence,” this means that the collectivization of information
and intellectual work can only be conceived of in relation to the market.

Diversity
Instead of Collectivity

I will
now briefly go into the components “intelligence” and ”collective.” Certain
approaches to “collective intelligence” break down the term intelligence very
specifically, as is the case in the neuro-/cognitive sciences[31] or
within the context of artificial intelligence research. In the management and
organizational theory discourses examined here, the term is basically
understood and used as a metaphor[32] for
the knowledge resources within an organization for instance or, as already
indicated above in relation to Douglas Engelbart’s work, for “problem solving”
and “task completion.”

Within
the context of the Web-oriented concepts discussed here, distinguishing between
“user generated content” and “collective intelligence” appears instructive. In
some projects, they both appear to be on the same level (Wikipedia appears as
an encyclopedia and simultaneously as “collective intelligence” in terms of
constant knowledge aggregation, as is reflected in the ceaseless process of
verifying, revising, editing the articles), though these aspects diverge more
widely in other areas. For instance, the production of “user generated
content”—like designing T-shirts on threadless.com, (which, unlike Wikipedia,
is a process of revision that eventually comes to an end, as decisions must be
made time and again, in order to physically produce the actual T-shirt)—seems
to be quite different from the prediction market context where the emphasis is
more strongly placed on the knowledge aggregation process.

In
this sense, we can speak of a broader definition of “collective intelligence”
that focuses on the cooperation perspective and comprises both aspects
mentioned above and equates “collective intelligence,” for instance, with “peer
production.”[33] In contrast,
a more concise definition of “collective intelligence” foregrounds the aspect
of knowledge aggregation and sets it apart from “user generated content.” For
instance, in James Surowiecki’s bestseller The Wisdom of Crowds, perhaps
the most influential book on “collective intelligence” to date, hardly touches
upon “user generated content” at all.

“Collective”:
while in the early 1990s, Pierre Lévy may have deemed it important to
distinguish “collective intelligence” from the fetishization of communities, at
the dawn of the 21st century “collective” appears to long since have
been devoid of political connotations. There were merely a handful of authors
who, when faced with such concepts, fell for the misconception and felt the
need to sound the alarm and come to the defense of the individual.

In the
end, for the most part, the term “collective” is consciously used as a
nondescript umbrella term, quite differently from how, for instance, the term
“group” is used—i.e. often in association with specific common characteristics,
such as that the members know each other. Within the context of “collective
intelligence,” the term commonly refers to one of four empirical forms of
collective contexts: online community, small groups (work teams),[34]
organization/company, and the “anonymous crowd” of Internet users[35].
(Their navigation data is rigourously
analyzed within the commercial sphere, while their “vast numbers” are just as
important for understanding certain aspects of “collective intelligence” as is
the recurrence of the power-law distribution that specifically draws attention
to the collective[36].)

“Online
community” is mainly defined by the fact that the orientation towards a common
interest and/or a long-term goal is the main focus. In this way, it differs
both from the traditional concept of community, which is defined by geographic
proximity, and it also differs from online social networks, which are not based
on one member’s relation to the collective interests, but rather on the
relation between two individuals.[37]

Both
viewpoints—the nature of the task/problem and the form of collectivity—are
closely related and have been analyzed in terms of their relation to one
another. Jeff Howe, for instance, states:

“The mechanics of
crowdsourcing content differ greatly from those that rely on collective
intelligence. In a prediction market or a crowdcasting network, the task is to
aggregate widely dispersed information and put it to good use. This presents
its own set of challenges. The crowd must be diverse, and nominally versed in
the relevant field, be it the sciences or the stock market. But the crowd
needn’t, generally speaking, interact with one another. In fact, [...]
interaction leads to deliberation, which in turn reduces the diversity of
thought through which collective intelligence thrives.
Crowdsourcing creative work, by contrast, usually involves cultivating a robust
community composed of people with a deep and ongoing commitment to their craft
and, most important, to one another.”[38]

While
this definition is not able to take the fine differences into account, it does
distinguish two basic models for exploiting collectivity. In relation to “user
generated content”—a less central element within the broader definition of
“collective intelligence”— in the form of the online community, where what is
interesting are the elements through which it emerges and which give it
stability and substance, namely the members’ long-term involvement, their
identification with the common goals, and the consolidation of relationships
among the members. At the same time, managers and more specifically
“[d]esigners and architects of communities”[39]
regard the online community—designable in detail using technical tools, where
each filtering option diversifies individual perceptions and each added level
of communication means that more complex tasks can be completed, etc.—from an
outside perspective, and the elements through which the communities emerge do
not enhance their self-determination, but instead their instrumentality,
thereby rendering the two modes indistinguishable from one another.

At the
center of a more narrow definition of “collective intelligence,” with an
emphasis on knowledge processes and information aggregation, lies a clearly
contrary model, where only one of the aspects of collectivity is deemed
absolute: diversity. The necessity of including diversity in considering “collective
intelligence” has by no means only been identified by Howe,[40]
moreover it is a widely recognized
fact. James Surowiecki, for instance, in The Wisdom of Crowds, lists
three requirements that must be fulfilled in order for a collective to be
considered “intelligent,” and the capacity to guarantee and sustain them is far
more fundamental than the desire to develop specific methods: diversity,
independence and decentralization.[41]

A
scientific basis for this can be found in the work of Scott E. Page, who
outlines these aspects of “collective intelligence” using methods from
complexity research. Already in the 1990s, while experimenting with agent-based
systems (software programs that each follow different
heuristics/problem-solving strategies), Page and his colleagues discovered
connections that brought about the so-called “diversity trumps ability”
theorem.[42] One example
for this is: a main unit of 1000 agents, from which a group of the twenty best
individual problem solvers and an equally large, randomly selected, comparison
group are compiled. The frequent repetition of such experiments has confirmed
that the comparison group regularly “outperforms” the group of the twenty best
individual problem solvers. Three years after Surowiecki’s Wisdom of Crowds,
Page published a book in which he describes and explains in detail the
mechanisms based on diversity that lead to this kind of outcome,[43] and,
in the meantime, has also presented a more comprehensive account of the
connections between diversity and complexity.[44]

This
concept of diversity, which is reduced to its functionality, tends to “extract”
aspects of collectivity that can be exploited in capitalist production, without
activating the other levels—solidarity, development of common goals, etc.—thus
enabling collective individuation.

To
return to the initial question: Is the term “collective intelligence” really
useful for leftist contexts? Yes. On the one hand, the term is more complex
than just this one area of discourse examined here. Also, we can expect terms
like “global brain” and “noopolitics”—despite all the problems that arise with
these concepts, such as the linear claim of the “global”—to be capable of
conceptualizing collectivity in an entirely different way, and that the concept
“collective intelligence” will increase both in its complexity and content by
combining diverse approaches.

As far
as the area from the discourse examined here is concerned, the main focus is on
the possibilities of utilizing the concept of “collective intelligence” as a
means to critique net capitalism—both its exploitation mechanisms as well as
the fragmented forms of collectivities that have come to inform society. A
further focus here is on what appropriations become possible by this “negation”,
and how this can directly be connected to a critique of the phenomena in
question.

In
this regard, an examination of these detailed studies of “diversity,”
especially in terms of what can be done with them, would certainly be
interesting. The fact that only fragmented forms of collectivity, which quickly
come into contradiction with one another, emerge within the models examined
here must be seen as situated within a context that seeks to exploit or
instrumentalize these collectivities. On the other hand, leftist movements use concepts
such as multitude/commons and precarity, both in theory and practice, to
develop complex forms of collective individuation and therefore also fully
different conditions for dealing with elements of collectivity.

If one
is able to avoid making the fatal mistake of mixing complexity research models
and “collective intelligence” concepts with critical engagements with
diversity, these insights could be used as a pool of micro-tools to be used on
both an organizational and tactical level to tease out the strengths of
diversity, particularly on these levels.

I would like to thank Lina Dokuzović, Therese Kaufmann und Gerald Raunig for
their advice and feedback.

[2] This argument is also thematically embedded in the short subchapter on
“Swarm Intelligence” in: Michael Hardt, Antonio Negri, Multitude. War and
Democracy in the Age of Empire. New York: Penguin Press 2004, pp. 91-92

[12] After all, largely because of the Vietnam War, it became quite clear
that the ARC was situated in the context of military funding. The computers
were used to coordinate the bombings in Vietnam, among other things, and
laser-guided bombs were being developed in a laboratory next to the ARC. (Cf. Schröter, Das Netz und die
virtuelle Realität, ibid., p. 77; Markoff, What the Dormouse Said, ibid.
p. 211.)

[21] Jeff Howe: Crowdsourcing. Why the Power of the Crowd is Driving the
Future of Business, New York: Three Rivers Press 2009, p. XI. On the one
hand—especially when used within a discourse so void of criticism—the
expression “home sweatshop” appears pleasingly bold. On the other hand, it is a
poor comparison and has the problematic potential to trivialize the situation
in “real” sweatshops.

[22] In this context, Howe also mentions a mode of production
behind open source software, the development of the technical tools, most
notably the Internet, and as the fourth element, “that transformed the first
three phenomena into an irrevocable force,” the online communities. (Cf. Howe,
Crowdsourcing, ibid., p. 99.)

[23] Carlo Vercellone, "The Crisis of the Law of Value
and the Becoming-Rent of Profit", in: Andrea Fumagalli, Sandro Mezzadra
(eds.), Crisis in the Global Economy: Financial Markets, Social Struggles,
and New Political Scenarios, Los Angeles: Semiotext(e) 2010, pp. 85-118.

[24] This is also a heading of a subchapter in Howe, Crowdsourcing, ibid.,
p. 37.

[25] And there are two areas—two mechanisms of how professionally
apprenticed/working people reach the status of amateurs—which are mentioned at
least briefly in the book, that Howe doesn’t mention in this context: besides
the somewhat euphemistically depicted structural unemployment in sectors such
as the art field, the general increase in unemployment, e.g. through the
financial crisis, that is briefly mentioned in the ”Status Update” of 2009, and
in addition, a field that is mentioned only once, probably because it can far
too easily be connected to “classical” outsourcing: professionals from the
Global South, appearing more or less as amateurs, whose chances for a regular
employment are withheld primarily due to the international division of labor.

[26]http://www.innocentive.com is a frequently cited example.
Companies post assignments and technical questions that their research
departments are unable to solve, come across a community of researchers,
engineers, self-taught experts, etc., who (not collectively, but each in
individual projects) who search for further solutions.

[29]http://tippie.uiowa.edu/iem/. The IEM were established by at the University of Iowa in the late
1980s for research and educational purposes and, among other things, used for
predictions of the outcomes of various elections, and, in 75% of cases, have
supposedly delivered better predictions of presidential elections in the US
than the large polling institutes.

[34] “This is important for two reasons. First, small groups are ubiquitous
in American life, and their decisions are consequential. Juries decide whether
or not people will go to prison. Boards of directors shape, at least in theory,
corporate strategy. And more and more of our work lives are spent on teams or,
at the very least, in meetings. Whether small groups can do a good job of
solving complex problems is hardly an academic question. Second, small groups
are different in important ways from groups such as markets or betting pools or
television audiences. Those groups are as much statistical realities as
experiential ones. Bettors do get feedback from each other in the form of the
point spread, and investors get feedback from each other in the stock market,
but the nature in the relationship between people in a small group is
qualitatively different.” (James Surowiecki, The Wisdom of Crowds. Why the
Many Are Smarter Than the Few, London: Abacus 2006 [First publication:
2004], p. 217)

[43] Page, The Difference, ibid. In Page’s work, the term
intelligence is, on the one hand, absolutely characterized primarily in the
above-mentioned sense by its problem-solving capacities, however it doesn’t
remain a metaphor, but instead is elaborated on in great detail.